Data Mining by Mehmed Kantardzic (inspirational novels TXT) 📗

considerably. The goals of prediction and description are achieved by using data-mining techniques, explained later in this book, for the following primary data-mining tasks:

1. Classification. Discovery of a predictive learning function that classifies a data item into one of several predefined classes.

2. Regression. Discovery of a predictive learning function that maps a data item to a real-value prediction variable.

3. Clustering. A common descriptive task in which one seeks to identify a finite set of categories or clusters to describe the data.

4. Summarization. An additional descriptive task that involves methods for finding a compact description for a set (or subset) of data.

5. Dependency Modeling. Finding a local model that describes significant dependencies between variables or between the values of a feature in a data set or in a part of a data set.

6. Change and Deviation Detection. Discovering the most significant changes in the data set.

The more formal approach, with graphical interpretation of data-mining tasks for complex and large data sets and illustrative examples, is given in Chapter 4. Current introductory classifications and definitions are given here only to give the reader a feeling of the wide spectrum of problems and tasks that may be solved using data-mining technology.

The success of a data-mining engagement depends largely on the amount of energy, knowledge, and creativity that the designer puts into it. In essence, data mining is like solving a puzzle. The individual pieces of the puzzle are not complex structures in and of themselves. Taken as a collective whole, however, they can constitute very elaborate systems. As you try to unravel these systems, you will probably get frustrated, start forcing parts together, and generally become annoyed at the entire process, but once you know how to work with the pieces, you realize that it was not really that hard in the first place. The same analogy can be applied to data mining. In the beginning, the designers of the data-mining process probably did not know much about the data sources; if they did, they would most likely not be interested in performing data mining. Individually, the data seem simple, complete, and explainable. But collectively, they take on a whole new appearance that is intimidating and difficult to comprehend, like the puzzle. Therefore, being an analyst and designer in a data-mining process requires, besides thorough professional knowledge, creative thinking and a willingness to see problems in a different light.

Data mining is one of the fastest growing fields in the computer industry. Once a small interest area within computer science and statistics, it has quickly expanded into a field of its own. One of the greatest strengths of data mining is reflected in its wide range of methodologies and techniques that can be applied to a host of problem sets. Since data mining is a natural activity to be performed on large data sets, one of the largest target markets is the entire data-warehousing, data-mart, and decision-support community, encompassing professionals from such industries as retail, manufacturing, telecommunications, health care, insurance, and transportation. In the business community, data mining can be used to discover new purchasing trends, plan investment strategies, and detect unauthorized expenditures in the accounting system. It can improve marketing campaigns and the outcomes can be used to provide customers with more focused support and attention. Data-mining techniques can be applied to problems of business process reengineering, in which the goal is to understand interactions and relationships among business practices and organizations.

Many law enforcement and special investigative units, whose mission is to identify fraudulent activities and discover crime trends, have also used data mining successfully. For example, these methodologies can aid analysts in the identification of critical behavior patterns, in the communication interactions of narcotics organizations, the monetary transactions of money laundering and insider trading operations, the movements of serial killers, and the targeting of smugglers at border crossings. Data-mining techniques have also been employed by people in the intelligence community who maintain many large data sources as a part of the activities relating to matters of national security. Appendix B of the book gives a brief overview of the typical commercial applications of data-mining technology today. Despite a considerable level of overhype and strategic misuse, data mining has not only persevered but matured and adapted for practical use in the business world.

1.2 DATA-MINING ROOTS

Looking at how different authors describe data mining, it is clear that we are far from a universal agreement on the definition of data mining or even what constitutes data mining. Is data mining a form of statistics enriched with learning theory or is it a revolutionary new concept? In our view, most data-mining problems and corresponding solutions have roots in classical data analysis. Data mining has its origins in various disciplines, of which the two most important are statistics and machine learning. Statistics has its roots in mathematics; therefore, there has been an emphasis on mathematical rigor, a desire to establish that something is sensible on theoretical grounds before testing it in practice. In contrast, the machine-learning community has its origins very much in computer practice. This has led to a practical orientation, a willingness to test something out to see how well it performs, without waiting for a formal proof of effectiveness.

If the place given to mathematics and formalizations is one of the major differences between statistical and machine-learning approaches to data mining, another is the relative emphasis they give to models and algorithms. Modern statistics is almost entirely driven by the notion of a model. This is a postulated structure, or an approximation to a structure, which could have led to the data. In place of the statistical emphasis on models, machine learning tends to emphasize algorithms. This is hardly surprising; the very word “learning” contains the notion of a process, an implicit algorithm.

Basic modeling principles in data mining also have roots in control theory, which is primarily applied to engineering systems and industrial processes. The problem of determining a mathematical model for an unknown system (also referred to